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N10: universe artefacts shipped with SHA-256; holdings CSV / company_tickers.json not archived; 2,573 -> 2,555 reconciled; missing filings; annual_panel = 2,499 CIKs

Items: FIRC0001-ADVE-15 AI_C-12 DATA-01 ECON-10 EDIT-16 LITE-09 THEO-14 (THEO-07, ECON-18).
data/metadata/{universe,russell3000_constituents,sp500_constituents}.json + SHA256SUMS; 18 dual-class CIKs explain 2,573 lines -> 2,555 CIKs;
7 unmatched tickers absent from ticker_to_cik (10,430 entries); 2,587 and the 14/21 drop counts not reconstructible;
2 filings missing on disk (XEL 10-K 2014, LNT 10-Q 2012) + 14 parse errors; annual_panel/quarterly_panel carry 2,499 CIKs (paper: 2,501).

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 5, 2026) parent c5c8715 Browse files

3 changed files +20,001 −0

3 changed files
Unified Split
added code/audit/a10_universe_artefacts.py +77 −0
@@ -0,0 +1,77 @@
1+"""N10 - universe artefacts: hashes, reconstructible counts, missing files, 'missing' filings, annual_panel CIKs (cluster H, I). Read-only."""
2+from __future__ import annotations
3+import json, sys, subprocess
4+from pathlib import Path
5+sys.path.insert(0, str(Path(__file__).resolve().parent))
6+from common import *
7+
8+r = Run("universe")
9+META = RDB / "data/metadata"
10+U = {}
11+files = ["universe.json", "russell3000_constituents.json", "sp500_constituents.json", "cik_mapping.json", "shards.json"]
12+U["sha256"] = {f: {"sha256": sha256_file(META / f), "bytes": (META / f).stat().st_size, "mtime": dt.datetime.fromtimestamp((META / f).stat().st_mtime).isoformat()} for f in files if (META / f).exists()}
13+with open(r.out / "SHA256SUMS", "w") as fh:
14+ for f, v in U["sha256"].items():
15+ fh.write(f"{v['sha256']} {f}\n")
16+# not archived?
17+found = subprocess.run(["find", str(RDB), "-maxdepth", "6", "(", "-iname", "*holding*", "-o", "-iname", "company_tickers*", "-o", "-iname", "*IWV*", ")", "-not", "-path", "*/.venv/*"], capture_output=True, text=True).stdout.strip().splitlines()
18+U["holdings_or_company_tickers_files_found"] = found
19+uni = json.load(open(META / "universe.json")); ru = json.load(open(META / "russell3000_constituents.json")); sp = json.load(open(META / "sp500_constituents.json")); cm = json.load(open(META / "cik_mapping.json"))
20+U["universe_keys"] = sorted(uni.keys()); U["russell_keys"] = sorted(ru.keys()); U["sp500_keys"] = sorted(sp.keys())
21+U["cik_mapping_type"] = type(cm).__name__; U["cik_mapping_size"] = len(cm)
22+U["cik_mapping_scalars"] = {k: v for k, v in cm.items() if not isinstance(v, (list, dict))} if isinstance(cm, dict) else None
23+U["cik_mapping_dict_sizes"] = {k: len(v) for k, v in cm.items() if isinstance(v, (list, dict))} if isinstance(cm, dict) else None
24+U["universe_scalars"] = {k: v for k, v in uni.items() if not isinstance(v, (list, dict))}
25+U["russell_scalars"] = {k: v for k, v in ru.items() if not isinstance(v, (list, dict))}
26+U["sp500_scalars"] = {k: v for k, v in sp.items() if not isinstance(v, (list, dict))}
27+cons = ru.get("constituents", [])
28+U["russell_constituent_lines"] = len(cons)
29+U["russell_distinct_ciks"] = len({c.get("cik") for c in cons})
30+U["russell_constituent_keys"] = sorted(cons[0].keys()) if cons else []
31+U["russell_unmatched"] = ru.get("unmatched")
32+dup = {}
33+for c in cons: dup.setdefault(c.get("cik"), []).append(c.get("ticker"))
34+U["russell_ciks_with_multiple_lines"] = {k: v for k, v in dup.items() if len(v) > 1}
35+# mapper view of the unmatched tickers
36+T2C = cm.get("ticker_to_cik", {}) if isinstance(cm, dict) else {}
37+def lookup(tkr):
38+ out = {"in_ticker_to_cik": T2C.get(tkr) or T2C.get(tkr.upper()) or T2C.get(tkr.replace(".", "-")) or T2C.get(tkr.replace("-", "."))}
39+ for k, v in (cm.items() if isinstance(cm, dict) else []):
40+ if isinstance(v, dict) and k != "ticker_to_cik" and (tkr in v or tkr.upper() in v):
41+ out[k] = v.get(tkr, v.get(tkr.upper()))
42+ if isinstance(v, list) and any(str(x).upper().startswith(tkr.upper()) for x in v[:100000] if isinstance(x, str)):
43+ out[k] = [x for x in v if isinstance(x, str) and str(x).upper().startswith(tkr.upper())][:3]
44+ return out
45+U["unmatched_mapper_view"] = {str(u): lookup(str(u).split(" ")[0]) for u in (ru.get("unmatched") or [])}
46+# is the unmatched ticker's company present in the universe under another ticker/CIK (e.g. HOLX)?
47+U["unmatched_in_universe_by_name"] = {str(u): [ (c.get("ticker"), c.get("cik"), c.get("company_name") or c.get("name")) for c in ucons0 if str(u).split("(")[-1].rstrip(")").split(" ")[0].upper() in str(c.get("company_name") or c.get("name") or "").upper()][:3] for u in (ru.get("unmatched") or [])} if (ucons0 := uni.get("constituents", [])) is not None else None
48+U["unmatched_in_edgar_companies"] = {}
49+ucons = uni.get("constituents", [])
50+U["universe_counts"] = {"n": len(ucons), "in_sp500": sum(1 for c in ucons if c.get("in_sp500")), "in_russell3000": sum(1 for c in ucons if c.get("in_russell3000")),
51+ "both": sum(1 for c in ucons if c.get("in_sp500") and c.get("in_russell3000")), "sp_only": sum(1 for c in ucons if c.get("in_sp500") and not c.get("in_russell3000")),
52+ "ru_only": sum(1 for c in ucons if c.get("in_russell3000") and not c.get("in_sp500")), "keys": sorted(ucons[0].keys()) if ucons else []}
53+U["sp500_rows"] = len(sp.get("constituents", [])); U["sp500_distinct_ciks"] = len({c.get("cik") for c in sp.get("constituents", [])})
54+U["reconciliation"] = {"iwv_equity_lines_reported_in_paper": 2587, "reconstructible": False, "reason": "latest-holdings.csv / company_tickers.json not archived; universe.py drops non-company lines with a bare continue",
55+ "lines_mapped": ru.get("count"), "distinct_ciks": U["russell_distinct_ciks"], "unmatched": len(ru.get("unmatched") or []), "universe_n": uni.get("count")}
56+# 'missing' filings and annual_panel CIKs
57+con = connect(SPID_DB); attach_edgar(con)
58+U["download_parse_status"] = con.execute("select form_type, download_status, parse_status, count(*) from edgar.filings group by 1,2,3 order by 1,2,3").fetchall()
59+r.dump("filings_status_missing.csv", con, "select accession_number, cik, ticker, form_type, filing_date, download_status, parse_status, local_path, file_size_bytes from edgar.filings where parse_status in ('missing','error') or download_status <> 'complete' order by parse_status, 1")
60+U["filings_missing_on_disk"] = con.execute("select form_type, count(*) from edgar.filings where parse_status='missing' group by 1").fetchall()
61+U["filings_parse_error"] = con.execute("select form_type, count(*) from edgar.filings where parse_status='error' group by 1").fetchall()
62+U["sections_for_error_filings"] = con.execute("select count(distinct accession_number) from edgar.filing_sections where accession_number in (select accession_number from edgar.filings where parse_status in ('error','missing'))").fetchone()[0]
63+U["paper_annual_panel_firms"] = 2501
64+U["annual_panel_distinct_ciks"] = con.execute("select count(distinct cik) from edgar.annual_panel").fetchone()[0]
65+U["filings_distinct_ciks"] = con.execute("select count(distinct cik) from edgar.filings").fetchone()[0]
66+U["companies_rows"] = con.execute("select count(*) from edgar.companies").fetchone()[0]
67+U["annual_panel_ciks_not_in_filings"] = con.execute("select list(distinct cik) from edgar.annual_panel where cik not in (select cik from edgar.filings)").fetchone()[0]
68+U["filings_ciks_not_in_annual_panel"] = con.execute("select list(distinct cik) from edgar.filings where cik not in (select cik from edgar.annual_panel)").fetchone()[0]
69+U["annual_panel_ciks_not_in_companies"] = con.execute("select list(distinct cik) from edgar.annual_panel where cik not in (select cik from edgar.companies)").fetchone()[0]
70+U["annual_panel_rows"] = con.execute("select count(*), min(fiscal_year), max(fiscal_year) from edgar.annual_panel").fetchone()
71+U["quarterly_panel_distinct_ciks"] = con.execute("select count(distinct cik) from edgar.quarterly_panel").fetchone()[0]
72+for u in (ru.get("unmatched") or []):
73+ tk = str(u).split(" ")[0]; nm = str(u).split("(")[-1].rstrip(")")
74+ U["unmatched_in_edgar_companies"][str(u)] = con.execute("select ticker, cik, company_name, in_sp500, in_russell3000 from edgar.companies where upper(ticker)=? or upper(company_name) like ? limit 3", [tk.upper(), "%" + nm.split(" ")[0].upper() + "%"]).fetchall()
75+U["companies_in_sp500_in_russell"] = con.execute("select count(*) filter (where in_sp500), count(*) filter (where in_russell3000), count(*) filter (where in_sp500 and in_russell3000), count(*) from edgar.companies").fetchone()
76+r.write_json("universe_artefacts.json", U)
77+r.finish()
added data/metadata/SHA256SUMS +5 −0
@@ -0,0 +1,5 @@
1+3967b29b378e50fbbac139bfc905120749ff09dcf8ec5769c3fbb12cb1c016e2 universe.json
2+758cbebf58540230ede17fd35890336ba6bf9e29fad58e3792ce7134997c6b41 russell3000_constituents.json
3+5084075260692130896c3c9a187223e2870b8e73774b35d1e28763fe8ffddb7d sp500_constituents.json
4+7ec59b276cef779f8f714d29d7b77938ee3731f11ea5bd95526006fd03ed20fc cik_mapping.json
5+8351ff54bea877175ca4bba51fc043ba279b2bfbcc7ddb80db1da534ff922771 shards.json
added data/metadata/russell3000_constituents.json +19919 −0
@@ -0,0 +1,28320 @@
1+{
2+ "source": "https://www.ishares.com/us/products/239714/ishares-russell-3000-etf/latest-holdings.csv",
3+ "holdings_as_of": "Oct 23, 2026",
4+ "fetched_at": "2026-10-27T16:14:42.459987+00:00",
5+ "count": 2573,
6+ "unmatched": [
7+ "TOWN (TOWNEBANK)",
8+ "FRBA (FIRST BANK)",
9+ "NBN (NORTHEAST BANK)",
10+ "HIFS (HINGHAM INSTITUTION FOR SAVINGS)",
11+ "ADRO (CHINOOK THERAPEUTICS INC)",
12+ "THRD (THIRD HARMONIC BIO INC)",
13+ "HOLX (HOLOGIC INC)"
14+ ],
15+ "constituents": [
16+ {
17+ "ticker_raw": "AAPL",
18+ "ticker": "AAPL",
19+ "company_name": "APPLE",
20+ "sector": "Information Technology",
21+ "location": "United States",
22+ "exchange": "NASDAQ",
23+ "weight_pct": 6.88,
24+ "market_value_usd": 1395685391.45,
25+ "cik": "0000320193"
26+ },
27+ {
28+ "ticker_raw": "NVDA",
29+ "ticker": "NVDA",
30+ "company_name": "NVIDIA",
31+ "sector": "Information Technology",
32+ "location": "United States",
33+ "exchange": "NASDAQ",
34+ "weight_pct": 6.74,
35+ "market_value_usd": 1366190461.44,
36+ "cik": "0001045810"
37+ },
38+ {
39+ "ticker_raw": "MSFT",
40+ "ticker": "MSFT",
41+ "company_name": "MICROSOFT",
42+ "sector": "Information Technology",
43+ "location": "United States",
44+ "exchange": "NASDAQ",
45+ "weight_pct": 5.33,
46+ "market_value_usd": 1081809742.65,
47+ "cik": "0000789019"
48+ },
49+ {
50+ "ticker_raw": "AMZN",
51+ "ticker": "AMZN",
52+ "company_name": "AMAZON.COM INC",
53+ "sector": "Consumer Discretionary",
54+ "location": "United States",
55+ "exchange": "NASDAQ",
56+ "weight_pct": 3.29,
57+ "market_value_usd": 667878136.0,
58+ "cik": "0001018724"
59+ },
60+ {
61+ "ticker_raw": "GOOGL",
62+ "ticker": "GOOGL",
63+ "company_name": "ALPHABET CLASS A",
64+ "sector": "Communication Services",
65+ "location": "United States",
66+ "exchange": "NASDAQ",
67+ "weight_pct": 2.55,
68+ "market_value_usd": 517974577.89,
69+ "cik": "0001652044"
70+ },
71+ {
72+ "ticker_raw": "GOOG",
73+ "ticker": "GOOG",
74+ "company_name": "ALPHABET CLASS C",
75+ "sector": "Communication Services",
76+ "location": "United States",
77+ "exchange": "NASDAQ",
78+ "weight_pct": 2.06,
79+ "market_value_usd": 418801350.72,
80+ "cik": "0001652044"
81+ },
82+ {
83+ "ticker_raw": "AVGO",
84+ "ticker": "AVGO",
85+ "company_name": "BROADCOM INC",
86+ "sector": "Information Technology",
87+ "location": "United States",
88+ "exchange": "NASDAQ",
89+ "weight_pct": 2.05,
90+ "market_value_usd": 415446503.2,
91+ "cik": "0001730168"
92+ },
93+ {
94+ "ticker_raw": "META",
95+ "ticker": "META",
96+ "company_name": "META PLATFORMS CLASS A",
97+ "sector": "Communication Services",
98+ "location": "United States",
99+ "exchange": "NASDAQ",
100+ "weight_pct": 1.93,
101+ "market_value_usd": 392148573.08,
102+ "cik": "0001326801"
103+ },
104+ {
105+ "ticker_raw": "TSLA",
106+ "ticker": "TSLA",
107+ "company_name": "TESLA INC",
108+ "sector": "Consumer Discretionary",
109+ "location": "United States",
110+ "exchange": "NASDAQ",
111+ "weight_pct": 1.49,
112+ "market_value_usd": 301863347.82,
113+ "cik": "0001318605"
114+ },
115+ {
116+ "ticker_raw": "LLY",
117+ "ticker": "LLY",
118+ "company_name": "ELI LILLY",
119+ "sector": "Health Care",
120+ "location": "United States",
121+ "exchange": "NYSE",
122+ "weight_pct": 1.39,
123+ "market_value_usd": 282243307.1,
124+ "cik": "0000059478"
125+ },
126+ {
127+ "ticker_raw": "BRK B",
128+ "ticker": "BRK-B",
129+ "company_name": "BERKSHIRE HATHAWAY INC CLASS B",
130+ "sector": "Financials",
131+ "location": "United States",
132+ "exchange": "NYSE",
133+ "weight_pct": 1.38,
134+ "market_value_usd": 280307892.48,
135+ "cik": "0001067983"
136+ },
137+ {
138+ "ticker_raw": "MU",
139+ "ticker": "MU",
140+ "company_name": "MICRON TECHNOLOGY",
141+ "sector": "Information Technology",
142+ "location": "United States",
143+ "exchange": "NASDAQ",
144+ "weight_pct": 1.35,
145+ "market_value_usd": 274617864.35,
146+ "cik": "0000723125"
147+ },
148+ {
149+ "ticker_raw": "JPM",
150+ "ticker": "JPM",
151+ "company_name": "JPMORGAN CHASE & CO",
152+ "sector": "Financials",
153+ "location": "United States",
154+ "exchange": "NYSE",
155+ "weight_pct": 1.21,
156+ "market_value_usd": 246127855.04,
157+ "cik": "0000019617"
158+ },
159+ {
160+ "ticker_raw": "AMD",
161+ "ticker": "AMD",
162+ "company_name": "ADVANCED MICRO DEVICES",
163+ "sector": "Information Technology",
164+ "location": "United States",
165+ "exchange": "NASDAQ",
166+ "weight_pct": 1.15,
167+ "market_value_usd": 233362725.98,
168+ "cik": "0000002488"
169+ },
170+ {
171+ "ticker_raw": "XOM",
172+ "ticker": "XOM",
173+ "company_name": "EXXONMOBIL HOLDINGS CORP",
174+ "sector": "Energy",
175+ "location": "United States",
176+ "exchange": "NYSE",
177+ "weight_pct": 1.03,
178+ "market_value_usd": 209400785.55,
179+ "cik": "0002115436"
180+ },
181+ {
182+ "ticker_raw": "V",
183+ "ticker": "V",
184+ "company_name": "VISA CLASS A",
185+ "sector": "Financials",
186+ "location": "United States",
187+ "exchange": "NYSE",
188+ "weight_pct": 0.95,
189+ "market_value_usd": 191991627.2,
190+ "cik": "0001403161"
191+ },
192+ {
193+ "ticker_raw": "JNJ",
194+ "ticker": "JNJ",
195+ "company_name": "JOHNSON & JOHNSON",
196+ "sector": "Health Care",
197+ "location": "United States",
198+ "exchange": "NYSE",
199+ "weight_pct": 0.87,
200+ "market_value_usd": 176472269.97,
201+ "cik": "0000200406"
202+ },
203+ {
204+ "ticker_raw": "WMT",
205+ "ticker": "WMT",
206+ "company_name": "WALMART",
207+ "sector": "Consumer Staples",
208+ "location": "United States",
209+ "exchange": "NASDAQ",
210+ "weight_pct": 0.72,
211+ "market_value_usd": 146095076.64,
212+ "cik": "0000104169"
213+ },
214+ {
215+ "ticker_raw": "ABBV",
216+ "ticker": "ABBV",
217+ "company_name": "ABBVIE",
218+ "sector": "Health Care",
219+ "location": "United States",
220+ "exchange": "NYSE",
221+ "weight_pct": 0.69,
222+ "market_value_usd": 140397487.16,
223+ "cik": "0001551152"
224+ },
225+ {
226+ "ticker_raw": "MA",
227+ "ticker": "MA",
228+ "company_name": "MASTERCARD CLASS A",
229+ "sector": "Financials",
230+ "location": "United States",
231+ "exchange": "NYSE",
232+ "weight_pct": 0.69,
233+ "market_value_usd": 139845418.0,
234+ "cik": "0001141391"
235+ },
236+ {
237+ "ticker_raw": "COST",
238+ "ticker": "COST",
239+ "company_name": "COSTCO WHOLESALE CORP",
240+ "sector": "Consumer Staples",
241+ "location": "United States",
242+ "exchange": "NASDAQ",
243+ "weight_pct": 0.61,
244+ "market_value_usd": 123612033.96,
245+ "cik": "0000909832"
246+ },
247+ {
248+ "ticker_raw": "PLTR",
249+ "ticker": "PLTR",
250+ "company_name": "PALANTIR TECHNOLOGIES CLASS A",
251+ "sector": "Information Technology",
252+ "location": "United States",
253+ "exchange": "NASDAQ",
254+ "weight_pct": 0.6,
255+ "market_value_usd": 121216297.75,
256+ "cik": "0001321655"
257+ },
258+ {
259+ "ticker_raw": "CVX",
260+ "ticker": "CVX",
261+ "company_name": "CHEVRON",
262+ "sector": "Energy",
263+ "location": "United States",
264+ "exchange": "NYSE",
265+ "weight_pct": 0.59,
266+ "market_value_usd": 120042282.19,
267+ "cik": "0000093410"
268+ },
269+ {
270+ "ticker_raw": "CSCO",
271+ "ticker": "CSCO",
272+ "company_name": "CISCO SYSTEMS INC",
273+ "sector": "Information Technology",
274+ "location": "United States",
275+ "exchange": "NASDAQ",
276+ "weight_pct": 0.58,
277+ "market_value_usd": 116812714.5,
278+ "cik": "0000858877"
279+ },
280+ {
281+ "ticker_raw": "INTC",
282+ "ticker": "INTC",
283+ "company_name": "INTEL CORPORATION",
284+ "sector": "Information Technology",
285+ "location": "United States",
286+ "exchange": "NASDAQ",
287+ "weight_pct": 0.54,
288+ "market_value_usd": 110299803.12,
289+ "cik": "0000050863"
290+ },
291+ {
292+ "ticker_raw": "UNH",
293+ "ticker": "UNH",
294+ "company_name": "UNITEDHEALTH GROUP",
295+ "sector": "Health Care",
296+ "location": "United States",
297+ "exchange": "NYSE",
298+ "weight_pct": 0.52,
299+ "market_value_usd": 104858644.36,
300+ "cik": "0000731766"
301+ },
302+ {
303+ "ticker_raw": "PG",
304+ "ticker": "PG",
305+ "company_name": "PROCTER & GAMBLE",
306+ "sector": "Consumer Staples",
307+ "location": "United States",
308+ "exchange": "NYSE",
309+ "weight_pct": 0.51,
310+ "market_value_usd": 104239839.44,
311+ "cik": "0000080424"
312+ },
313+ {
314+ "ticker_raw": "KO",
315+ "ticker": "KO",
316+ "company_name": "COCA-COLA",
317+ "sector": "Consumer Staples",
318+ "location": "United States",
319+ "exchange": "NYSE",
320+ "weight_pct": 0.49,
321+ "market_value_usd": 99972530.56,
322+ "cik": "0000021344"
323+ },
324+ {
325+ "ticker_raw": "MRK",
326+ "ticker": "MRK",
327+ "company_name": "MERCK & CO INC",
328+ "sector": "Health Care",
329+ "location": "United States",
330+ "exchange": "NYSE",
331+ "weight_pct": 0.49,
332+ "market_value_usd": 99715652.32,
333+ "cik": "0000310158"
334+ },
335+ {
336+ "ticker_raw": "BAC",
337+ "ticker": "BAC",
338+ "company_name": "BANK OF AMERICA",
339+ "sector": "Financials",
340+ "location": "United States",
341+ "exchange": "NYSE",
342+ "weight_pct": 0.45,
343+ "market_value_usd": 91093446.4,
344+ "cik": "0000070858"
345+ },
346+ {
347+ "ticker_raw": "HD",
348+ "ticker": "HD",
349+ "company_name": "HOME DEPOT",
350+ "sector": "Consumer Discretionary",
351+ "location": "United States",
352+ "exchange": "NYSE",
353+ "weight_pct": 0.44,
354+ "market_value_usd": 88567963.26,
355+ "cik": "0000354950"
356+ },
357+ {
358+ "ticker_raw": "CAT",
359+ "ticker": "CAT",
360+ "company_name": "CATERPILLAR INC",
361+ "sector": "Industrials",
362+ "location": "United States",
363+ "exchange": "NYSE",
364+ "weight_pct": 0.43,
365+ "market_value_usd": 86583984.0,
366+ "cik": "0000018230"
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